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Snapback MCP server

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by ra1labsworkx-wq·io.github.ra1labsworkx-wq/snapback·v1.8.4
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Diagnose why an AI agent failed and get the verified fix instantly. Free, no token.

B79/100grade B
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Safety scan
B79/100

full report

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If you have run it, two minutes of your experience saves the next person an afternoon.

Snapback tools (23, 1 write)

write = sends, deletes, buys or posts
  • agent_memoryFree

    See what YOU (this agent) tend to fail on — your recurring failure patterns across past diagnoses. Returns your top failure classes with counts and what share of your failures each is (e.g. 'loop_repeated_tool_call: 12 times, 40%'). Call it before a run to know what to guard against. Token-scoped to your own agent. Free.

  • budget_guardFree

    Live MID-RUN budget check (fast, no LLM). Send whatever counters you have and get an advisory on context %, token burn, cost burn, step budget, and off-task drift - with concrete suggested_actions and the projected cost of NOT acting. Call it every few steps to catch a runaway BEFORE you hit a limit. Advisory only (never blocks). Params: step, max_steps, tokens_used, token_budget, cost_used_usd, cost_budget_usd, context_used, context_window.

  • cascade_rootFree

    Given an ORDERED list of errors from a run (oldest first), find the TRUE root — the error that cascaded or shouldn't have been retried — not just the final symptom you see. E.g. a 429 that got retried and triggered a downstream 401: the root is the 429, not the 401. Each error can be a string or {error, retried, action}. Free, no token, no LLM.

  • convert_traceFree

    Turn your raw logs into a Snapback trace so you don't hand-craft JSON. Pass 'source' = a list of log/step entries, or an object with a spans/steps/messages/events/logs array (OTel spans, OpenAI/LangChain message lists, or generic {tool,input,output} arrays all work). Returns {trace} ready to pass straight to diagnose_trace. Free, no token.

  • detect_loopFree

    MID-RUN loop check (fast, no LLM, free). Send your recent steps DURING a run; get back whether you're stuck repeating a tool call and a concrete next move. Call this every few steps to catch a loop BEFORE you burn your step budget — don't wait for a postmortem. Non-blocking advice, not a verdict.

  • diagnose_batchFree

    Diagnose SEVERAL traces in one call (up to 20). Each trace is metered like a separate diagnose_trace. Returns a verdicts array (per-trace, order preserved); a bad trace in the batch is isolated and doesn't fail the rest. Use for post-run analysis of many failures at once instead of N round-trips.

  • diagnose_infra_errorFree

    Diagnose a cryptic AGENT-INFRASTRUCTURE error — payments (x402/EIP-3009), Solana on-chain (ATA, blockhash, compute), MCP protocol, RPC/providers, EVM & Solana wallets, library of VERIFIED fixes the base model often gets wrong. Pass the error text (and optional context). Returns {matched, family, failure_class, root_cause, fix, confidence, source, action_class, auto_safe, gate} instantly from the library (no LLM) when a known pattern matches. Free and no token. GATE CONTRACT for autonomous action: auto-apply the fix ONLY when confidence>=0.85 AND source=='library' AND auto_safe==true (action_class is retry|refetch|config); NEVER auto-apply a 'mutate' or 'destructive' fix (create/change state, money, auth) — escalate those to a human. gate.auto_apply_ok gives you the verdict. Use it the moment you hit a transaction_simulation_failed, BlockhashNotFound, a -32xxx RPC code, a 4001/-32002 wallet error, an attestation failure, or any infra error you can't instantly resolve.

  • diagnose_traceFree

    Diagnose why an AI agent run failed. Returns a structured verdict (failure_class, failed_at_step, root_cause, fix_suggestion, confidence). LATENCY: known patterns return library-instant (<1s); a NOVEL failure needs an LLM call and can take up to ~25s — set your client timeout to at least 30s, and treat this as async (don't block your agent loop on it).

  • get_request_statusFree

    Check what happened to a pattern you requested (from request_pattern's request_id). Returns pending / approved / rejected / in_library so you can see if your suggestion was actioned — the feedback loop isn't a black box. Free, no token.

  • get_verdictFree

    Fetch a previously produced verdict by its id or by trace_id (your org only).

  • my_impactFree

    See how your feedback + pattern requests have shaped the shared library — how many verdicts you've rated, patterns you've requested, and how many were approved into the library. Turns your input into visible collaboration. Token-scoped to you.

  • my_usageFree

    See YOUR current usage + remaining allowance so you can self-govern spend: snapbacks (diagnoses) used/cap/remaining, guard checks used/cap/remaining, estimated spend, % used, and when it resets. Call it periodically to avoid surprises. Token-scoped, free.

  • preflightFree

    BEFORE running: get known failure patterns for a given agent setup so you can avoid them. Returns a ranked list of {failure_class, root_cause, fix_suggestion} from Snapback's library. Call this before executing a plan and self-correct.

  • recommend_failoverFree

    Should you RETRY the same target, SWITCH provider, FALL BACK to another chain, or STOP? Pass the error + your configured topology (current_chain, available_chains, and/or current_provider, available_providers) and get deterministic routing advice with the reason — so a flaky Solana RPC doesn't get retried 5x when you should switch to Base. Free, no token, no LLM. Reads only what you tell it (no network calls).

  • report_outcomeFree

    Report the outcome of an auto-applied fix (the self-heal interceptor calls this after it gate-applied a fix and retried). Pass failure_class, family, fix, confidence, action_class, and succeeded (did the retry work?). TWO purposes: it's your safety telemetry (spot a fix that didn't work) AND it feeds the shared crowd view — every reported outcome makes what_others_did sharper for the next agent. Token-scoped (so we know it's your org), free.

  • request_patternFree

    Leave us a message: ask us to add a failure pattern to the library, or report a problem we couldn't diagnose well. Use this when diagnose_trace didn't have a good answer, when you keep hitting a failure we don't classify, or when you want a specific kind of problem supported. It goes straight to our roadmap/backlog. Free — no token needed.

  • search_docsFree

    Search Snapback's documentation for how to use it — how to format a trace, what each tool does, the failure taxonomy, auth, pricing, and errors. Free and needs no token. Call this first if you're unsure how to format a request or what a verdict means.

  • session_endFree

    Close a live session and get a short run summary (total steps, duration). Frees the session. Free, no token.

  • session_startFree

    Open a LIVE mid-run session so Snapback can watch your run step-by-step and warn you in real time (loop / token / cost / context) - the always-on guardian mode. Returns a session_id. Free, no token. Call session_step as you run, session_end when done.

  • session_stepFree

    Report ONE step of a live run and get back any warnings immediately (loop detected / budget breach). Pass the step (action + inputs) and any counters you have (step, max_steps, tokens_used, token_budget, cost_used_usd, cost_budget_usd, context_used, context_window, task, recent_actions. context_window). Warnings are advisory - act on them to self-correct mid-run. Free.

  • submit_feedbackwrite actionFree

    Tell Snapback whether a verdict was correct (correct=true/false), with an optional free-text note (did the fix work? what was wrong?). ONE rating per verdict — call it AFTER you act on a verdict and see the outcome. Your correction updates the SHARED pattern library (fix patterns are shared anonymized so every agent benefits; your trace content is never shared) — a right verdict comes back faster next time, a wrong one gets down-weighted. To ask for a new pattern or report an unsolved problem, use request_pattern instead.

  • suggest_budget_recoveryFree

    Approaching a token/context/cost budget mid-run? Pass your counters and get the LEAST-DISRUPTIVE recovery ranked: truncate context | switch to a cheaper model | batch steps | wrap up — scored by speed gained, accuracy lost, cost saved. Turns budget_guard's 'you're at 94%' into 'here's what to do about it'. Free, no token, no LLM.

  • what_others_didFree

    THE CROWD: for a failure_class (or pass the family/error and we'll map it), see what OTHER agents did about the same failure and whether it worked — anonymized, aggregated across everyone. Returns {total, agree_pct (community success rate), distinct_orgs, sample_fixes (fixes rated CORRECT by other agents)}. Use it when you hit a failure and want the crowd's verdict on what actually fixes it, not just the single library answer. Free, no token. Privacy-safe: only aggregate counts + a community success rate + the working fixes — never any org, agent, or trace identity. Hidden below a small min-sample so a single report can't be reverse-engineered. This is the network effect: the more agents use Snapback, the sharper this answer gets.

Public scan report

scanner v0.1.10 · 2026-10-31 · same rubric, same numbers if you re-run it

1 high
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 1832ms20/20
  • Tool poisoning23 tool descriptions checked15/15
  • Auth qualityopen endpoint exposes 1 write-action tools with no auth3/15
  • Maintenancelast push 29 days ago15/15
  • Maintainer identityregistry namespace matches repository owner6/10

Findings (1)

  • highWrite-action tools reachable without authenticationauth.open-write
Overall 79/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Snapback compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
79/100
higher than 58% of 4,460 AI and agents servers
Tools
23
the typical AI and agents server has 6
Tools that change things
4%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote AI and agents server answers 100%
Response time
872 ms
faster than 12% of 2,331 AI and agents servers

What its tools let an agent do: 22 read · 1 change

Release history: 4 versions in the registry since 2026-09-21, latest 2026-10-02, typically one every 1 day.

Install Snapback in Claude Code, Cursor or VS Code

claude mcp add --transport http snapback https://api.snapback.sh/mcp
Add to Cursor

What the publisher says

From the Snapback repository's README, as published. We do not edit it. Read it on GitHub

Snapback self-heal interceptor for OpenClaw

Make Snapback automatic. Instead of remembering to call the diagnosis tool, wrap your agent's tool calls once — and every error is auto-diagnosed, and gated-safe fixes are applied and retried without a human.

"Power tools are optional. Infrastructure is not." This turns Snapback from a tool you reach for into

infrastructure that runs on every failure.

What it does

On any tool-call error, the interceptor:

  1. Calls diagnoseinfraerror (free, no token, no LLM, <150ms).
  2. Applies the three-factor gate — auto-apply a fix only when all are true:
  • confidence >= 0.85
  • source == "library" (a curated verified fix, not an LLM guess)
  • auto_safe == true (the fix is retry / refetch / config — reversible, side-effect-free)

verdict). It never auto-applies a mutate or destructive fix (create/change state, money, auth grants).

  1. If the gate passes → applies the fix and retries once. If it fails → escalates to a human (logs the

It fails closed: when unsure, it escalates rather than acting.

Install

# no install needed beyond the stdlib; drop the file in, or:
cp snapback_interceptor.py your_agent/
export SNAPBACK_MCP=https://api.snapback.sh/mcp   # default; the free tools need no token

Use

from snapback_interceptor import SnapbackInterceptor

heal = SnapbackInterceptor(
    auto_apply=True,
    on_escalate=lambda v: my_ops_inbox.log(v),   # human sees non-auto-safe verdicts
    on_heal=lambda v: metrics.count("snapback.autoheal", family=v["family"]),
)

# wrap any call that might raise:
result = heal.run(call_solana_rpc, wallet, amount)

# or as a decorator:
@heal.guard
def call_solana_rpc(wallet, amount): ...

# or manually on a caught error:
verdict = heal.diagnose("BlockhashNotFound")
if verdict["gate"]["auto_apply_ok"]:
    ...apply verdict["fix"] and retry...

The gate contract (returned by Snapback)

diagnoseinfraerror returns {matched, family, fix, confidence, source, actionclass, autosafe, gate}. action_class is one of:

gate.autoapplyok is the ready-made verdict; the interceptor also re-derives it locally so a stale client can't over-trust.

Privacy & safety

  • 100% client-side. Talks only to the free public diagnoseinfraerror endpoint.
  • Never sends your Snapback token for the diagnosis (diagnosis is free).
  • Snapback scrubs trace content server-side; this sends only the error string you pass.
  • Composes with bridge.py (post-mortem forwarding) — this is the live auto-heal layer.

Roadmap

  • submit_feedback on the retry outcome (did the fix work?) → feeds the shared library (the network effect).
  • Hermes + LangChain adapters (same gate, different framework hook).

<!-- mcp-name: io.github.ra1labsworkx-wq/snapback -->

Nothing above is checked by us. What we check is on the safety report.

Snapback: common questions

Is Snapback MCP server safe?
Mostly: it is graded B (79/100). Read the Snapback safety report
How do I install Snapback?
It runs remotely at api.snapback.sh. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does Snapback need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Snapback maintained?
The last commit was 33 days ago (2026-10-02). The latest release is v1.8.4.
Is Snapback up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of Snapback?
Servers from other publishers that do the same job: Commonly MCP server, mcptoon MCP server and llmtrim MCP server.
How does Snapback compare with other AI and agents MCP servers?
safety score 79/100 (higher than 58% of 4,460 AI and agents servers); tools 23 (the typical AI and agents server has 6); tools that change things 4% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

Alternatives to Snapback

Same job from other publishers: the closest match first, then the best rated.

  • Commonly
    Read Commonly pods and post as a Commonly agent you create, with a token from your Commonly account.
    A
  • mcptoon
    MCP tools + agent skills in one zero-dependency CLI: 71,929 -> 581 tokens (-99.2%, measured).
    A
  • llmtrim
    MCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.
    A
  • Agent Lsp
    Orchestrates language servers into 65 code-intelligence tools across 32 languages, token-optimized.
    A